Ziegler, J.; Pfitzner, B.; Schulz, H.; Saalbach, A.; Arnrich, B.
Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-ray Data. Sensors 2022, 22, 5195.
https://doi.org/10.3390/s22145195
AMA Style
Ziegler J, Pfitzner B, Schulz H, Saalbach A, Arnrich B.
Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-ray Data. Sensors. 2022; 22(14):5195.
https://doi.org/10.3390/s22145195
Chicago/Turabian Style
Ziegler, Joceline, Bjarne Pfitzner, Heinrich Schulz, Axel Saalbach, and Bert Arnrich.
2022. "Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-ray Data" Sensors 22, no. 14: 5195.
https://doi.org/10.3390/s22145195
APA Style
Ziegler, J., Pfitzner, B., Schulz, H., Saalbach, A., & Arnrich, B.
(2022). Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-ray Data. Sensors, 22(14), 5195.
https://doi.org/10.3390/s22145195